NetApp Instaclustr for MCP Gateway Is Here: Smarter AI Data Access Starts Now

AI systems are only as good as the data they can access. But for most organizations, getting AI applications to reliably connect with the right tools, services, and data sources has meant stitching together custom integrations, managing inconsistent access patterns, and hoping nothing breaks when you scale.

For enterprise AI teams, the challenge is even bigger. AI agents need secure, governed, and scalable access to business-critical data without creating another layer of integration sprawl.

That changes today.

NetApp is announcing Instaclustr for Model Context Protocol (MCP) Gateway, a production-ready, fully managed solution that gives AI applications and agents standardized, governed, and scalable access to your data infrastructure.

What is MCP Gateway?

An MCP Gateway is a standardized access layer that lets AI applications and agents securely connect to enterprise tools, databases, APIs, and data services using the Model Context Protocol. Instead of building custom integrations for every data source, teams can use an MCP Gateway to centralize access control, governance, observability, and routing.

For organizations scaling MCP for enterprise AI, the gateway becomes the controlled path between AI agents and the data infrastructure they depend on.

The Problem: Enterprise AI Without a Standard is a Mess

Most teams building AI-powered applications run into the same problem: there’s no standard way for AI systems to connect to data. Every new source becomes its own integration project, with one team building a connector, another wiring up an internal API, and a third patching in a vector store. Over time, those one-off integrations turn into technical debt that gets harder to maintain, harder to scale, and harder to debug when something fails.

This inconsistency creates bigger problems for AI agents and large language models. They need reliable, predictable access to tools and context to work well. When data handoffs are exposed in ad hoc ways, agents behave unpredictably, miss key context, and fail to complete tasks. The issue isn’t the model itself. It’s the infrastructure underneath it. As enterprise AI adoption grows, fragmented integrations and siloed systems also drive up operational overhead, slow deployments, and stretch time to value from weeks into months.

Custom integration sprawl also weakens governance and security. When every connection is built differently, it becomes difficult to enforce consistent access controls, rotate credentials properly, or ensure sensitive business data is handled safely. Without a centralized data access layer, security and compliance become inconsistent by default. The result is an AI stack that drains engineering time, increases risk, and slows innovation instead of accelerating it.

Introducing Instaclustr for MCP Gateway

Instaclustr for MCP Gateway is a fully managed gateway service that brings MCP to your existing Instaclustr-managed data infrastructure — and connects it to the AI applications and agents your teams are building.

Instead of building and maintaining individual connectors between your AI systems and your data, you deploy the MCP Gateway once. It becomes the single, standardized access point that your AI applications use to reach databases, search services, message queues, and other infrastructure — all through a governed, secure, and observable layer.

MCP Gateway gives you a production-ready front door for all your agents. You can group tools behind OAuth, see which agent called what and when and give only the access you want.

Instaclustr for MCP gateway also lets you define parametrised tools for your LLMs so they won’t have free reign to drop tables, over-write sensitive data or otherwise cause mischief, they can only do what you define based on configured permissions.

A managed AI data access layer for MCP-compatible agents

Instaclustr for MCP Gateway acts as a managed AI data access layer between MCP-compatible agents and enterprise data services. It gives teams one governed path to expose data, tools, and operational systems to AI applications without relying on fragile point-to-point integrations.

For enterprise AI teams, that means less custom integration work, stronger control over data access, and a more reliable foundation for building agentic applications.

Built on AgentGateway

The Instaclustr MCP Gateway is built on top of the Linux Foundation project AgentGateway. We choose AgentGateway not just because of its strong technical foundations, but also for the great community support and ownership by the Linux Foundation.

Thanks to the project team for discussing with us and reviewing our contributions to the project.

Initial Launch

MCP Gateway ships with the operational characteristics Instaclustr customers expect:

• OAuth 2.0 / OIDC authentication for agent and persona endpoints with SCIM provisioning
• Configurable access to underlying resources, including human- and LLM-readable tool descriptions
• Error checks to protect back-end clusters and APIs
• Audit logging of gateway usage available on the console or shipped to your logging solution
• Monitoring and alerting integrated with the Instaclustr monitoring pipeline
• 24×7 expert support and production SLAs (up to 99.9% availability)
• SOC 2 compliance for the managed platform

Agents connect over streamable HTTP to the gateway. You can define personas, groupings of tools exposed to a class of agent (for example, “Customer Support” vs “Order Operations”) each with its own OAuth client, so many bots can share one policy surface without sharing credentials.

The initial launch supports Instaclustr for Apache Kafka and REST APIs, we plan to then release connection to all Instaclustr technologies, Cassandra, OpenSearch, PostgreSQL, ClickHouse and Cadence in the coming months. If you need a capability on an accelerated timeline contact your Instaclustr account team.

How MCP Gateway works

The gateway sits between your AI applications and your managed data services. When an AI agent needs to query a database or retrieve context from a search index, it sends a structured MCP request to the gateway. The gateway authenticates the request, applies your defined access policies, routes it to the appropriate backend service, and returns a structured response.

Your AI applications don’t need to know the specifics of each backend. The gateway handles that. You define the rules once and enforce them consistently across every request.

What you get: Benefits of Instaclustr for MCP Gateway

Simplified integration across your data stack

One gateway replaces dozens of custom connectors. Any MCP-compatible AI application can connect to your managed data services through a single, standardized interface. Onboarding new data sources becomes a configuration task, not an engineering project.

Stronger control and governance

Define access policies, permissions, and data-handling rules in one place. Every request through the gateway is authenticated, logged, and subject to your governance controls — giving you the visibility and auditability that fragmented integrations simply can’t provide.

Faster time to value

Stop spending weeks on integration plumbing. Teams can move from AI prototype to production in days rather than months, with data access that’s already secure, observable, and production-ready from the start.

Better developer productivity

Developers building AI applications don’t need deep knowledge of every backend system they’re connecting to. The gateway abstracts that complexity. They write against the MCP standard, and the infrastructure handles the rest. Less context-switching, fewer dependencies to manage, faster iteration.

Improved scalability

As your AI footprint grows — more agents, more models, more data sources — the gateway scales with it. You’re not rebuilding integrations every time you add a new capability. The architecture is designed to support growth without adding proportional operational burden.

More reliable AI experiences

Consistent data access means more consistent AI behavior. When your agents know they’ll get well-formed, timely responses from the tools they depend on, the entire system becomes more predictable — and the end-user experience improves as a result.

Built on the Instaclustr Managed Platform

Instaclustr for MCP Gateway is built on the same managed platform that organizations already rely on for PostgreSQL, Cassandra, OpenSearch, Kafka, ClickHouse, and more. That means you get the same SLAs, the same support model, and the same operational reliability — extended now to your AI data access layer.

There’s no separate infrastructure to maintain. No additional vendor to manage. If you’re already running data services with Instaclustr, you’re one step away from giving your AI systems the access they need.

“MCP is quickly becoming the standard way LLMs interact with structured data and operational systems. Most valuable information still lives in databases and pipelines and the services around them. MCP Gateway lets our customers expose that world to agents with Instaclustr-grade reliability without risking cluster health or security.”

-Paul Aubrey, Director of Product Management, NetApp Instaclustr

Standardize your AI data access layer

The gap between AI capabilities and AI reliability often comes down to data infrastructure. Teams that build on fragmented, ungoverned integrations spend more time maintaining them than moving forward. Those that adopt a standardized access layer — one that enforces governance, simplifies connectivity, and scales without friction — build better AI systems faster.

Instaclustr for MCP Gateway gives you that foundation.

Ready to get started?

If are ready to standardize the way your AI applications access data using the Instaclustr MCP Gateway Service take the next steps by:

• Provisioning the MCP Gateway from the Instaclustr console, Terraform, or API
• Exploring the documentation to see how the gateway connects to your existing Instaclustr-managed services
• Talk to our team to discuss your specific AI data access needs and how MCP Gateway fits your architecture – reach out to [email protected]

The infrastructure for reliable AI is here. Let’s build on it.